High resolution ECG-aided early prognostic model for comatose survivors of out of hospital cardiac arrest

Association Between Postresuscitation 12-Lead ECG Features and Early Mortality After Out-of-Hospital Cardiac Arrest: A Post Hoc Subanalysis of the PEACE Study

Background Once the return of spontaneous circulation after out-of-hospital cardiac arrest is achieved, a 12-lead ECG is strongly recommended to identify candidates for urgent coronary angiography. ECG has no apparent role in mortality risk stratification. We aimed to assess whether ECG features could be associated with 30-day survival in patients with out-of-hospital cardiac arrest. Methods and Results All the post-return of spontaneous circulation ECGs from January 2015 to December 2018 in 3 European centers (Pavia, Lugano, and Vienna) were collected. Prehospital data were collected according to the Utstein style. A total of 370 ECGs were collected: 287 men (77.6%) with a median age of 62 years (interquartile range, 53-70 years). After correction for the return of spontaneous circulation-to-ECG time, age >62 years (hazard ratio [HR], 1.78 [95% CI, 1.21-2.61]; P=0.003), female sex (HR, 1.5 [95% CI, 1.05-2.13]; P=0.025), QRS wider than 120 ms (HR, 1.64 [95% CI, 1.43-1.87]; P<0.001), the presence of a Brugada pattern (HR, 1.49 [95% CI, 1.39-1.59]; P<0.001), and the presence of ST-segment elevation in >1 segment (HR, 1.75 [95% CI, 1.59-1.93]; P<0.001) were independently associated with 30-day mortality. A score ranging from 0 to 26 was created, and by dividing the population into 3 tertiles, 3 classes of risk were found with significantly different survival rate at 30 days (score 0-4, 73%; score 5-7, 66%; score 8-26, 45%). Conclusions The post-return of spontaneous circulation ECG can identify patients who are at high risk of mortality after out-of-hospital cardiac arrest earlier than other forms of prognostication. This provides important risk stratification possibilities in postcardiac arrest care that could help to direct treatments and improve outcomes in patients with out-of-hospital cardiac arrest.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Background

More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known.

Methods and Results

We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c‐statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was −1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out‐of‐hospital cardiac arrest score (9 validations; median c‐statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c‐statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c‐statistic, 0.76).

Conclusions

Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.